Startup profile for Tecton: latest funding, latest known valuation, total disclosed funding, investors, status, sources, and why the company is interesting. Part of the Venture Capital Tracker startup directory.

Startup profile · funding coverage

Tecton funding, valuation and investors

Enterprise feature store for real-time ML data (acquired by Databricks 2025).

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Funding, valuation & investors

Answer-first snapshot

Latest funding

Series C

Amount not disclosed · 2022

Latest known valuation

$900M

Series C · 2022

Total disclosed equity funding

Not publicly disclosed

Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.

Current status

acquired

acquired · San Francisco, California, USA

Investors in latest funding

Other: Sequoia , Kleiner Perkins

Sources: latest funding Last verified: 2026-07-25

Overview

Tecton developed an enterprise feature store for real-time machine learning — helping teams define, compute, and serve ML features for fraud detection, personalization, and risk scoring. Founded by creators of Uber's Michelangelo ML platform, Tecton raised from Sequoia and Kleiner Perkins and was last valued at $900 million in 2022. In August 2025, Databricks announced it would acquire Tecton to integrate real-time data serving with Agent Bricks and AI agent workflows. Deal terms were not disclosed; consideration included Databricks private shares.

Why Tecton is interesting

Tecton founders built Uber Michelangelo — Databricks bought the feature store to power Agent Bricks and real-time AI agents after $900M 2022 valuation.

Product & use cases

Tecton's feature store lets ML teams define, share, and serve features for both batch and real-time inference — bridging data engineering and model deployment.

  • Real-time fraud detection feature pipelines
  • Personalization models requiring low-latency feature serving
  • Enterprise ML platforms integrating with Databricks post-acquisition

Key facts

  • Acquired by Databricks (Aug 2025) for Agent Bricks integration (Reuters)
  • Last private valuation $900M (2022)
  • Founded by Uber Michelangelo creators
  • ~90 employees at acquisition

Funding history (newest first)

Investors in our directory

Funds linked from Tecton's profile — open a fund page for stage focus and related deal articles.

Competitive landscape

Edge: Team pedigree from Uber Michelangelo plus production-grade real-time serving — trusted by Coinbase and joint Databricks customers.

Feature stores became infrastructure table stakes for production ML. Databricks' acquisition consolidates data platform and real-time serving for agentic AI workloads.

  • Feast (open source) alternative

    OSS feature store; less enterprise support.

  • Snowflake Feature Store adjacent

    Cloud data platform native features; Tecton also partnered with Snowflake.

  • Databricks native features direct

    Post-acquisition integration target.

Notable stories

  • Databricks and Snowflake both invested in and partnered with Tecton before Databricks acquired it — classic platform consolidation in ML infrastructure (Reuters, Aug 2025).

Industries

AI & Machine Learning Enterprise SaaS Infrastructure & Cloud

FAQs about Tecton

Practical answers founders, operators, and investors typically search for.

Tecton provides an enterprise feature store for real-time machine learning feature engineering and serving.
Databricks announced acquisition in August 2025.
Sequoia, Kleiner Perkins, and a16z among backers. See /fund/sequoia, /fund/kleiner-perkins, /fund/andreessen-horowitz.
To integrate real-time feature serving with Agent Bricks and AI agent workflows.
Last reported at $900M in 2022 private funding.
Feast is open-source; Tecton offered enterprise managed feature store with real-time serving.
No — joining Databricks; deeper integration planned.
Team includes creators of Uber's Michelangelo ML platform.

By Venture Capital Tracker

Last updated:

Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.